A Novel Phase Enhancement Method for Low-Angle Estimation Based on Supervised DNN Learning

Abstract

In low-altitude target situation, the multi-path signals cause the amplitude-phase distortion of direct signal from targets and degrade the performance of existing methods. Hence, in this paper, we propose a phase enhancement method for low-angle estimation using supervised deep neural network (DNN) to mitigate the phase distortion, thus to improve direction of arrival (DOA) estimation accuracy. The mapping relationship between the original phase difference distribution of the received signal and desired phase difference distribution is learned by DNN during training. The phase of test data is enhanced by trained DNN, and the enhanced phase is used for DOA estimation. We explain the significance of enhancing phase instead of amplitude by discussing the sensitivity of amplitude and phase on DOA estimation. Moreover, we prove the effectiveness and superiority of the proposed method by simulation experiments. The results demonstrate that the proposed technique has a better performance in terms of estimation error and goodness of fit (GoF) than the physics-driven DOA estimation methods and state-of-the-art methods including feature reversal and the support vector regression (SVR). INDEX TERMS Phase enhancement, supervised deep neural network, DOA estimation.

Deep Learning Approach in DOA Estimation: A Systematic Literature Review

“…SNR directly affects the performance of the super-resolution DOA estimation algorithm. As shown in Table 6, increasing the SNR from −5 dB to 5 dB in [51] reduces its RMSE from 0.08 to 0.02. Increasing the SNR from −5 dB to 5 dB in [53] reduces its RMSE from 3.3 to 0.3, while the RMSE drops from 1.3 to 0.28 when increasing the SNR from −10 dB to 10 dB in [43].…”

“…DOA estimation is an important task in signal processing, and it has a wide range of applications in fields such as radar, sonar, and so on. Xiang et al [51] analyzed the unknown multipath signal and concluded that the unknown multipath signal severely distorts the phase characteristic distribution of the desired signal. e designed supervised deep neural network is used for phase enhancement, thereby effectively reducing the phase distortion.…”

“…If the ratio in [41] increases from 1/20 to 5/20, the MAE increases from 0.35 to 1.02. In [51], if the ratio increases from 1/20 to 1/10, the accuracy will drop from 100% to 97%; if the ratio in [44] increases from 1/8 to 3/8, the RMSE increases from 0.01 to 0.018. From the above relationship, the DOA estimation performance will improve as the ratio increases.…”

Time Reversal Detection in Multistatic Radar System

“…For example, when the false alarm probability is 0.01 and Monte-Carlo experiments number is 100,000, the detection threshold is the 1000-th value in the test statistics sequence arranged in descending order. When the target exists, an echo is constructed according to (8) or (17). Then 100,000 Monte-Carlo experiments are run to calculate the test statistics and subsequently compared to the detection threshold to determine whether the target exists or not.…”

“…Compared with the monostatic radar, the multistatic radar system utilizes spatial diversity to improve the detection performance [3]- [6]. There exist lots of multipaths for urban environments or low angle target detection [7], [8], which severely reduces the detection probability [9]. In order to solve the multipath problem in detection, the multistatic scattering model in multipath environment was studied [10].…”